Learning to Clarify Underspecified Intents Under Limited Interaction

Excerpt

arXiv:2610.04719v1 Announce Type: new Abstract: AI assistants receive requests that leave out information needed for a good outcome, for example about users' preferences or goals. They must then either speculate or ask for more information before proceeding. We reconceptualize this as a value-of-information problem: the assistant should acquire information whose absence causes the greatest avoidable loss in user utility. This is rarely known ex ante; rather, assistants must predict it in order t